Pathologic Vertebral Fractures (PVF), associated with intractable pain, loss of function and severe neurologic compromise in up to 50% of patients with spinal metastases, represent the most dreaded complication associated with this condition. Lung, breast and prostate cancer are the leading causes of cancer death in the western world and vertebral bone is the most frequent site of skeletal metastasis. Current guidelines for estimating fracture risk for pathologic vertebra remain poorly defined with low specificity and sensitivity. The quantitative computed tomography structural analysis protocol (QCT-SAP) for prediction of pathologic vertebral fracture relies upon computing bone modulus and strength based on the relationship between CT number and bone density. By quantifying the effect of the lesion on the strength of the cancellous bone (architecture and density) and the structural load carrying capacity of the vertebra (cross sectional properties), we and others have shown this method to offer improved predictive accuracy compared to that reported for current radiological guidelines. However this CT - bone modulus relationship was established for non-pathologic bone only, and it may not account for the deleterious effects of metastatic lesions on the composition and material properties as well as architectural efficiency of the vertebral bone, that forms a critical portion of load carrying capacity of the human vertebra. Based on our experimental, image analysis and computational work, we will apply a multiscale approach to determine the deleterious effect of lytic (LM) and blastic (BM) metastasis on the compositional and material properties and architectural efficiency of vertebral bone. These properties will be compared to bone obtained from adjacent vertebrae showing no radiographic evidence of metastasis (internal control) and from age/sex matched vertebral levels from donors with no history of cancer (external control). Using both experimental and computational models we will establish LM- and BM- specific relationships between CT and bone modulus and implemented these models in QCT-SAP. In addition, for the lesion specific models, we will incorporate patient specific factors (co-morbidities, radiation, antiresorptive treatment) found to significantly affec the model. We will then test whether the improvement in prediction accuracy of the lesion specific QCT- SAP exceeds 10%, a difference deemed to be clinically important. Finally, we will compare the improved accuracy of the lesion and patient specific QCT-SAP to that of the current non-specific QCT-SAP, in predicting the structural response of complete lumbar human spines under functional loads. This aim establishes whether metastatic specific material models significantly improve the accuracy of CT-SAP in predicting failure of pathologic spines. By providing quantitative, sensitive and objective, assessment of the change in the skeletal status of the patient, we expect this novel approach to enable robustly informed treatment decisions and thus improve patient management and outcome.